Comparison of the local pivotal method and systematic sampling for national forest inventories
Comparison of the local pivotal method and systematic sampling for national forest inventories
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国家森林清查局部关键方法与系统抽样的比较
DOI：
10.1186/s40663-020-00266-9
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发表时间：
2020
影响因子：
4.1
通讯作者：
J. Heikkinen
中科院分区：
文献类型：
--
作者：
M. Räty;M. Kuronen;M. Myllymäki;A. Kangas;K. Mäkisara;J. Heikkinen
背景利用辅助数据进行样本选择的局部枢轴法(LPM)最近被提出作为国家森林资源清查(NFIs)的一种抽样方法。与简单随机抽样(SRS)和地理坐标下的LPM相比，它的性能在仿真研究中产生了令人满意的结果。在这项模拟研究中，我们将所有这些抽样方法与系统抽样进行了比较。仅使用坐标(LPMxy)或以遥感为基础的辅助森林变量(RS变量)选择LPM样本。我们使用现场测量数据(NFI-field)和多源NFI(MS-NFI)图作为目标数据，独立的MS-NFI图作为辅助数据。使用相对效率(RE)进行比较，即参考抽样设计与研究设计的均方误差之比。在NFI中应用一种方法还需要一个经验证的方差估计器。因此，三种不同的方差估计量根据重复试验的经验方差进行了评估：1)对应于SRS的估计量；2)重新用于LPM的Grafström-Schelin估计量；以及3)在芬兰NFI中用于系统抽样设计的Matérn估计量。结果在大多数目标变量上，LPMxy与系统设计基本一致。根据所研究的目标变量，使用辅助数据的LPM设计与系统设计相比的Res在0.74-1.18之间变化。SRS方差估计器出人意料地是最有偏见和最保守的估计器。同样，在LPMxy的情况下，Grafström-Schelin估计量给出了过高的估计。当RS变量被用作辅助数据时，Grafström-Schelin估计往往低估了经验方差。在系统抽样中，Matérn估计量和Grafström-Schelin估计量的实际效果是一样的。结论针对特定变量优化的LPM往往比系统抽样更有效，但所有考虑的LPM设计对某些目标变量的效率都低于系统抽样设计。在系统抽样中，Grafström-Schelin估计量可以与LPMxy一样使用，也可以代替Matérn估计量。如果要在LPM中使用其他辅助变量，则需要对方差估计进行进一步的研究。
Background The local pivotal method (LPM) utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories (NFIs). Its performance compared to simple random sampling (SRS) and LPM with geographical coordinates has produced promising results in simulation studies. In this simulation study we compared all these sampling methods to systematic sampling. The LPM samples were selected solely using the coordinates (LPMxy) or, in addition to that, auxiliary remote sensing-based forest variables (RS variables). We utilized field measurement data (NFI-field) and Multi-Source NFI (MS-NFI) maps as target data, and independent MS-NFI maps as auxiliary data. The designs were compared using relative efficiency (RE); a ratio of mean squared errors of the reference sampling design against the studied design. Applying a method in NFI also requires a proven estimator for the variance. Therefore, three different variance estimators were evaluated against the empirical variance of replications: 1) an estimator corresponding to SRS; 2) a Grafström-Schelin estimator repurposed for LPM; and 3) a Matérn estimator applied in the Finnish NFI for systematic sampling design. Results The LPMxy was nearly comparable with the systematic design for the most target variables. The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18, according to the studied target variable. The SRS estimator for variance was expectedly the most biased and conservative estimator. Similarly, the Grafström-Schelin estimator gave overestimates in the case of LPMxy. When the RS variables were utilized as auxiliary data, the Grafström-Schelin estimates tended to underestimate the empirical variance. In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally. Conclusions LPM optimized for a specific variable tended to be more efficient than systematic sampling, but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables. The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling. Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.